A Particulate Method for Determining Residence Time in Viscous Flow Processes
Bibliographic record
Abstract
Abstract It is well known that residence time distribution (RTD) is a significant parameter in material processing, therefore, its accurate prediction is essential. From a numerical standpoint, one of the most widespread simulation methodologies is based on the combination of computational fluid dynamics (CFD) and particle tracking techniques. Within this framework, the novelty of this contribution is that the RTD is built upon a minimization of a least square criterion ensuring an accurate prediction of the mean residence time, as well as a smooth RTD function. In addition, a computational procedure is developed to estimate the thickness of the near‐wall region, which must be free of particles otherwise the RTD predictions will diverge. The relevance of the proposed method is assessed on analytical flow test cases and the Kenics static mixer for both Newtonian and non‐Newtonian fluids. Good agreement is found between numerical and corresponding theoretical mean residence times. magnified image
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".